170results about "Adaptive network" patented technology

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention relates to the technical field of telephone communication, and discloses a robust recursive least square adaptive filter based on inverse QR decomposition and a storage medium, and the method comprises the steps: calculating an inner product of an adaptive weight vector at a moment and an input signal vector, and obtaining an output signal at the moment of the adaptive filter; calculating a difference value between the expected signal and the output signal at the moment, obtaining an estimation error signal, and constructing a nonlinear weighting function; recursively updating and acquiring an inverse correlation matrix of the moment input signal vector based on the moment input signal vector and transpose thereof, a nonlinear weighting function, the inverse correlation matrix of the input signal vector and the covariance of the estimated error signal, and constructing a block matrix containing the inverse correlation matrix of the moment input signal vector; inverse QR matrix decomposition and unitary rotation operation are sequentially carried out on the block matrix, an array before inverse QR decomposition and an array after inverse QR decomposition at the moment are obtained, a gain vector at the moment is constructed, and a self-adaptive weight vector at the moment is updated and obtained.

The invention relates to the technical field of electronic circuits, in particular to a filter circuit, a voltage filter control circuit, a chip and electronic equipment. The filter circuit comprises a counter, a first data selector, a digital operation circuit and a register, the counter is used for receiving the counting enable signal, outputting a counting mark value to the first data selector, determining a corresponding current logic operation mode according to the current counting mark value, and performing logic operation on the intermediate input parameter according to the current logic operation mode to obtain a first voltage error value after filtering processing; by adopting the filter circuit provided by the invention, required logical operation can be completed by virtue of the first data selector and the digital operation circuit, so that the first voltage error value is output, and the filtered voltage signal is adjusted according to the first voltage error value, so that the set frequency component in the output voltage signal is filtered out; in this way, complex filter coefficients do not need to be configured, a processor with higher computing power does not need to be used, and hardware cost is saved.

The invention relates to the technical field of radio frequency signal processing, and discloses a digital filtering repair method and system for signal distortion, and the method comprises the steps: obtaining a frequency domain amplitude-frequency response and a phase response; constructing a phase compensation amount by adopting piecewise polynomial fitting and a phase inversion mechanism; a complex number logarithm domain mapping mechanism is adopted to execute composite frequency response inversion; constructing a switchable hybrid digital filter with an amplitude path and a phase path coupled in parallel; multi-band adaptive scheduling is executed; and outputting the radio frequency baseband signal stream. The technical problems that in the prior art, frequency response nonlinear distortion in a broadband range cannot be dealt with by adopting a fixed template or a single-path filter, and particularly, a high-precision and low-delay dynamic compensation effect cannot be realized in a scene that multi-band coupling interference exists in a radio frequency ADC channel with the bandwidth exceeding 800MHz are solved. Due to the fact that the switchable hybrid digital filter is constructed, accurate modeling and flexible recovery of distortion characteristics in broadband signals are achieved.

The invention relates to the technical field of signal processing, and discloses an anti-interference electrically tunable wave trap operation control method and system, and the method comprises the steps: collecting an input signal of an adjustable wave trap group, and employing adaptive Kalman filtering to dynamically suppress strong electromagnetic noise to construct a reinforcement learning state vector, and generating an optimal action instruction of the tuning module by using the deep Q network reinforcement learning model so as to control the tuning module to perform parameter adjustment on the adjustable wave trap group, and finally updating and adjusting the deep Q network reinforcement learning model and the adaptive Kalman filtering based on an actual effect index. Therefore, the electrically tunable wave trap high-precision operation control method based on adaptive Kalman filtering and depth Q network depth collaboration is constructed, the electrically tunable wave trap can be helped to stably maintain high-precision wave trapping performance in a scene of complex dynamic interference and strong noise superposition, and the performance of the electrically tunable wave trap is improved. And many limitations of the traditional scheme in the aspects of interference adaptation, precision control, multi-frequency coordination and stability are thoroughly solved.

The invention relates to the technical field of servo control, in particular to a servo driving current harmonic compensation method and system for electromagnetic interference suppression. The method comprises the following steps: extracting common-mode and differential-mode interference by using current and voltage sampling and combining differential measurement, and configuring a multi-stage passive filter according to spectrum characteristics to construct a suppression channel; establishing a digital interference cancellation mechanism, generating a reverse compensation signal by adopting self-adaptive trapped wave and a DSP (Digital Signal Processor), and injecting the reverse compensation signal into a control loop to form a software-defined multi-channel suppression architecture; after interference suppression, a high-order tensor model is constructed for the purified three-phase current, and an alternating least square method is adopted for decomposition so as to separate multi-band harmonic waves; a graph neural network is introduced, harmonic coupling is represented, and dominant and residual harmonic decoupling is realized through message passing; according to a grading progressive strategy, dominant harmonic waves are firstly subjected to coarse compensation, then residual harmonic waves are finely adjusted, and a compensation sequence is adaptively optimized through iterative feedback, so that high-robustness closed-loop control of servo driving is realized.

The invention relates to the technical field of digital filters, and discloses a quantization sensing wide linear minimum error mode adaptive filter, which performs non-uniform low-bit quantization on an input signal and an expected signal by using a quantization parameter to obtain a corresponding quantization value; constructing a linear factor based on the quantization parameter; based on the non-roundness of the input signal, constructing a normalized correlation matrix of the input signal and the linearized error signal, and constructing a compensation matrix in combination with a linear factor; obtaining input signal quantized values at a current moment and a historical moment, constructing an augmented input signal vector, calculating a product of the augmented input signal vector and the compensation matrix, and constructing a corrected input vector; performing wide linear filtering on the corrected input vector to obtain an output signal, and calculating an error signal in combination with an expected signal quantized value; and updating the weight vector at the next moment by using the corrected input vector and the error signal. According to the invention, by introducing a non-uniform low-bit quantization and deviation compensation mechanism, the cost and energy consumption of the analog-to-digital converter are remarkably reduced, and a relatively low steady-state error is kept.

The invention provides a learning enhanced Kalman filtering method for forest emergency positioning and a related device. The method comprises the following steps: acquiring time sequence measurement data of an unmanned aerial vehicle and a ground target node; inputting time sequence measurement data into an error compensation network, and outputting a ranging error compensation amount and an observation noise covariance matrix; performing distance measurement compensation on the time sequence distance measurement data, feeding back the distance measurement data subjected to error compensation and a state error of the ground target node to a state prediction network, and outputting a state transition matrix and a process noise covariance matrix; state prediction is carried out based on a state transition model and a process noise covariance matrix, Kalman filtering fusion is carried out on a prediction state, compensated distance measurement data and an observation noise covariance matrix, three-dimensional position estimation of a ground target node is determined, combined improvement of measurement correction and prior prediction precision is realized, and the accuracy of measurement correction and prior prediction is improved. On the basis of keeping the optimality of traditional filtering, modeling is carried out on time-varying noise and non-linear motion, and the positioning precision in a forest complex environment is improved.

The invention relates to the technical field of interference suppression, in particular to an electromagnetic compatibility optimization method and system for a ship shaft frequency converter, and the method comprises the steps: carrying out the term-by-term judgment of sensitive interference energy, current errors and switching loss after the discretization and jump limitation based on a frequency sequence, and screening out a boundary-crossing sequence, the genetic algorithm executes cross combination and amplitude superposition in the residual sequence, a new frequency set is formed through transformation and rearrangement, frequency points are distributed in a plurality of intervals to be reorganized, the switch state is predicted and updated one by one through Kalman filtering, observation residual errors are corrected in real time, and the frequency points are reorganized. The harmonic distortion rate, the radiation intensity and the instruction change amplitude are synchronously output by taking the time index as a sequence, the deviation is compensated at the amplitude and phase level, the harmonic index and the radiation index are controlled in an agreed range, the switching loss and the instruction change are within threshold values, and a complete closed loop covering frequency generation, harmonic correction and sequence updating is formed.

The invention discloses a robust adaptive fusion filtering astronomical attitude determination method and system, and belongs to the technical field of radio navigation. The method comprises the following steps: constructing an integrated navigation model of a starlight and inertial integrated navigation system based on Lie group description; obtaining an observation residual error and an observation residual error covariance of a corresponding mode; robust and adaptive mode parallel sub-filtering is carried out, and posterior states and covariances in the two modes are updated; updating the mode probability; and fusing the updated posterior state with the covariance to obtain fusion output of parallel sub-filtering, taking the fusion output of the parallel sub-filtering as final estimation of the starlight and inertial integrated navigation system based on Lie group description, and outputting an astronomical attitude determination result containing an azimuth angle, a pitch angle and a roll angle. The high-precision astronomical attitude determination method has strong adaptability, strong robustness and accuracy when coping with complex noise and measuring outliers, and can realize high-precision astronomical attitude determination of starlight and inertial integrated navigation in a complex dynamic environment.

The invention relates to the technical field of offshore wind power monitoring, in particular to an offshore wind power jacket foundation structure fatigue life intelligent monitoring system which comprises a signal acquisition module, a data preprocessing module, a fatigue feature extraction module, a fatigue damage calculation module, a life prediction module and a result output module. The signal acquisition module acquires a mechanical response signal of a jacket foundation structure and transmits the signal to the data preprocessing module, the data preprocessing module transmits the signal to the fatigue feature extraction module, the fatigue feature extraction module extracts feature parameters and transmits the feature parameters to the fatigue damage calculation module, and a fatigue damage value is calculated and transmitted to the life prediction module. The life prediction module predicts residual life and transmits a prediction result to the result output module, and the result output module outputs residual life data. According to the method, accurate monitoring and safe and economic operation and maintenance of the fatigue life of the jacket foundation are realized through arrangement of specific sensors, data processing, extraction of fusion features, damage calculation and model prediction and early warning.

The invention provides a segmented adaptive SG filtering method and device based on cosine weight transition. The method comprises the following steps: obtaining a noisy fault signal of a power distribution network; extracting local feature indexes of the signals, wherein the local feature indexes comprise an average gradient representing the change frequency of the signals and a composite feature index representing the noise level; dynamically determining a window length and a polynomial order of SG filtering based on the local feature indexes; the transient stage is the first 1 / 2 period of the post-fault signal, the steady-state stage is the subsequent part of the post-fault signal, an overlapping region is arranged at the junction of the transient stage and the steady-state stage, the overlapping region is a 1 / 2 period following the transient stage, and a cosine weight function is adopted to perform weighted fusion on filtering results of the two stages in the overlapping region; and splicing the filtering results of the non-overlapping region and the overlapping region of the transient stage and the non-overlapping region of the steady stage, and outputting a filtered fault feature signal.

The invention provides an LSTM-Kalman filtering method and device for unmanned aerial vehicle magnetic compensation, and belongs to the technical field of aeromagnetic signal processing, and the method comprises the steps: obtaining a reference geomagnetic field vector in a zero interference environment, and synchronously collecting an unmanned aerial vehicle attitude angle, OBE motor rotation speed, phase current, power supply noise and magnetometer data; oBE represents airborne electrons; removing a static magnetic field according to a reference value to obtain an effective magnetic disturbance variable, performing low-pass filtering, performing dynamic normalization on all features by using a sliding window, and constructing a time sequence feature matrix; inputting the time sequence characteristic matrix into a trained LSTM neural network, and outputting a magnetic interference predicted value; and calculating a residual error between the measured value and the predicted interference value to obtain a predicted true value, carrying out Kalman filtering smooth estimation on the predicted true value, and outputting a compensated geomagnetic field vector. According to the invention, integrity compensation of global magnetic disturbance is realized.

The application discloses a micro blood flow imaging method based on adaptive density filtering and a related device, and relates to the technical field of ultrasonic imaging. The method comprises the following steps: extracting a static frame in original ultrasonic data, screening a high-quality static frame through correlation analysis to eliminate motion interference; performing SVD clutter filtering, and cutting off high spatiotemporal coherence singular values to separate tissue clutter and microbubble signals; performing adaptive density filtering and subtracting a preliminary filtering result to realize noise suppression and overlapping microbubble decoupling; and enhancing microbubble motion signals through time-lag autocorrelation processing, and finally completing power Doppler imaging and ultrasonic localization microscope reconstruction. The scheme effectively solves the problem that the prior art cannot simultaneously consider PDI noise suppression and ULM microbubble decoupling, significantly improves image signal-to-noise ratio and super-resolution, avoids problems such as signal distortion, artifacts and loss of low-speed blood flow, and provides more reliable imaging support for clinical diagnosis such as blood vessel lesion detection and tumor blood flow evaluation.

The invention provides an elastic wave CT (Computed Tomography) signal denoising method for improving the inversion precision of karst cave exploration, which belongs to the technical field of engineering exploration and comprises the following steps of: performing normalization preprocessing on a received signal, suppressing random noise by adaptive filtering, converting a time domain signal into an F-K domain and identifying a surface wave, filtering a surface wave signal by a Gaussian band elimination filter, and converting the F-K domain signal back to the time domain. The method comprises the following steps of: highlighting a take-off characteristic of a weak P wave covered by a strong surface wave through normalization; using a minimum mean square error adaptive filter to accurately filter random noise; subsequently converting to an F-K domain, and smoothly attenuating the surface wave through a Gaussian band elimination filter by utilizing the frequency wave number distribution difference of the surface wave and the P wave; the method realizes layered removal of random noise and surface waves, finally outputs high-purity direct P-wave signals, greatly improves P-wave travel time pickup and wave velocity calculation precision, and remarkably improves inversion precision of an elastic wave CT exploration method in detection of geologic bodies such as karst caves and fracture zones.

The invention discloses a micro blood flow imaging method based on adaptive density filtering and a related device, and relates to the technical field of ultrasonic imaging, the method comprises the following steps: extracting static frames in original ultrasonic data, and screening high-quality static frames through correlation analysis to eliminate motion interference; sVD clutter filtering is carried out, and the high space-time coherence singular value is cut off to separate tissue clutters and microbubble signals; noise suppression and overlapped microbubble decoupling are realized through adaptive density filtering and subtraction with a preliminary filtering result; and enhancing a microbubble motion signal through time-delay self-correlation processing, and finally completing power Doppler imaging and ultrasonic positioning microscope reconstruction. According to the scheme, the problem that PDI noise suppression and ULM microbubble decoupling cannot be considered at the same time in the prior art is effectively solved, the image signal-to-noise ratio and super-resolution are remarkably improved, the problems of signal distortion, artifacts, low-speed blood flow loss and the like are avoided, and more reliable imaging support is provided for clinical diagnosis such as vasculopathy detection and tumor blood flow evaluation.

The application provides a lithium battery modeling and temperature estimation method based on starfish optimization and adaptive filtering, belongs to the technical field of lithium battery modeling, and solves the technical problem that accurate estimation of the temperature of a lithium battery cannot be realized in the case that noise covariance is unknown or inaccurate, thereby causing modeling difficulty, and the technical scheme is as follows: including the following steps: step 1) constructing a lithium battery variable parameter lumped thermal model based on the thermoelectric similarity principle to obtain model description of an identification system; step 2) constructing a starfish optimization iterative identification algorithm SFOA to accurately estimate unknown parameters of the lithium battery variable parameter lumped thermal model; and step 3) constructing a fading factor adaptive filter FFAKF to accurately estimate the temperature of the lithium battery thermal model. The application realizes lithium battery temperature estimation in the case that noise covariance is unknown or difficult to obtain.

This invention discloses an adaptive filtering method based on the multi-kernel Nystrom method, belonging to the field of kernel adaptive filtering. The multi-kernel Nystrom method combines multiple kernel functions, which can include not only Gaussian kernels but also other different kernel functions, mapping the original data to multiple different independent feature spaces. Compared with the traditional single-kernel Nystrom method, the multi-kernel Nystrom method has better filtering accuracy and becomes less sensitive to the choice of kernel parameters. Compared with other multi-kernel adaptive filtering methods, it can significantly reduce computational complexity, time, and memory consumption. Subsequently, this invention introduces the proposed multi-kernel Nystrom method into kernel adaptive filters for the first time. Experiments show that this algorithm can achieve better filtering accuracy than current state-of-the-art kernel adaptive filtering algorithms with relatively low computational complexity.

The invention provides an anti-pinch method and device for an electric sunshade curtain, a storage medium and an electronic device.The method comprises the steps that the electric sunshade curtain is started, and a first current signal of a driving motor of the electric sunshade curtain is collected; obtaining a first high-pass filtering signal and a first low-pass filtering signal of the first current signal passing through a low-pass filter; judging whether the electric sunshade curtain is in an anti-pinch state or not according to the first high-pass filtering signal and the first low-pass filtering signal; if the electric sunshade curtain is in the anti-pinch state, the driving motor is controlled to drive the electric sunshade curtain to rotate reversely to the full-open position. According to the embodiment of the invention, the current signal of the motor is detected, and anti-pinch identification is carried out according to the fact that the current signal passes through the high-pass filtering signal with the high cut-off frequency and the low-pass filtering signal with the low cut-off frequency, so that the interference of motor voltage and environment temperature is reduced, and the adaptive capacity of an anti-pinch scene is improved; the technical problem that in the prior art, an electric sunshade curtain cannot accurately recognize the anti-pinch state is solved.

The application discloses a satellite communication filtering method based on a handheld terminal and belongs to the technical field of satellite communication. The application firstly performs EMD decomposition on a satellite communication signal collected by the handheld terminal, obtains a plurality of intrinsic mode functions and a residual signal, and extracts energy signals of the intrinsic mode functions; then, first and second time-domain attenuation components are respectively calculated through neighborhood ratio detection and range impact detection, time-domain attenuation weight signals are obtained through fusion, and frequency-domain attenuation weight signals are calculated according to the energy signal in-band and out-of-band total energy difference value; each intrinsic mode function is processed by using a trained lightweight neural network, adaptive attention weighting is realized in combination with the time-domain and frequency-domain attenuation weight signals, a reconstructed signal is obtained, the reconstructed signal is superimposed with the residual signal, and finally, a filtered signal is output. The application effectively improves the satellite communication signal filtering precision through the time-domain and frequency-domain two-dimensional weight constraint combined with the lightweight neural network feature focusing.

The invention provides a corrugated low-pass filter design method and device.The method comprises the steps that an input matching section used for input waveguide port matching, a main suppression section used for wide stop-band suppression and an output matching section used for outlet waveguide port matching are sequentially arranged in the electromagnetic wave direction to form an arrangement structure, the arrangement structure is symmetrical along the narrow side gap of the first width; arranging the band elimination units in the arrangement structure at equal intervals, determining an initial trend of a main suppression section envelope line by using an incremental function, determining initial trends of input matching section and output matching section envelope lines by using a windowing function, and determining heights of all the band elimination units according to the initial trends; and keeping the height of the band elimination unit unchanged, compressing the narrow-side gap at the center position of the filter from the first width to the second width, and adaptively adjusting the width change of the narrow-side gap to obtain the ripple low-pass filter. According to the invention, an additional impedance converter is not needed, a stop band covering a complete frequency band can be formed, and a high-frequency spurious mode is effectively suppressed.

This filtering method comprises steps in which: a Kalman gain is calculated; a system calculates an estimated value on the basis of the Kalman gain; a predicted value and a measured value of an error covariance are compared and analyzed to calculate the error covariance; an exponentially weighted average and an exponentially weighted covariance for the estimated value are calculated; and the exponentially weighted average and the exponentially weighted covariance of the calculated estimated value are reflected in the subsequent Kalman gain calculation. Therefore, performance superior to that of a conventional Kalman filter can be achieved in non-Gaussian noise conditions.

This invention discloses a method and system for adaptive cancellation of strong and weak signals based on complex structures. The method includes: using a complex RLS adaptive filtering algorithm to track and filter strong interference signals in a complex observation signal to obtain a filtered output signal; during filtering, applying a new filter coefficient vector obtained iteratively from the current complex observation signal to the input complex observation signal after a delay of N periods, thereby decoupling the filter coefficient update from the signal filtering in time, allowing the filtering and filter coefficient update processes to be executed asynchronously; subtracting the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals; and extracting weak signal features from the cancelled signal. This invention fully utilizes the complete information of complex signals in amplitude and phase, overcoming the limitations of traditional real-number processing methods in separating overlapping spectrum signals. It is particularly suitable for complex scenarios where strong and weak signals have similar spectra and large power differences, and has the advantages of high precision, low distortion, and strong real-time performance.

The invention relates to the technical field of clock synchronization, in particular to a clock synchronization method based on a self-adaptive dynamic adjustment Kalman filter, which comprises the following steps of: defining a state vector containing clock skew and drift by adopting a second-order clock model, constructing a state transition and observation model, and calculating the state of the state transition and observation model; adaptively estimating a noise covariance based on a historical residual sliding window; measuring and predicting residual errors are recorded in each round of filtering, and the prediction residual errors are quantized; the residual error is stored in a sliding window, sample variance is calculated to obtain noise covariance initial estimation, and meanwhile, a simplified model of the proportional relation between clock skew and drift noise covariance is assumed; according to a covariance matching principle, using maximum likelihood estimation to calculate observation and process noise covariance from a sliding window residual error, that is, measuring a residual error square mean value as an observation noise covariance, presenting the process noise covariance in a matrix form, and accurately estimating noise characteristics; and performing exponential weighted moving average updating on the noise covariance by using a smoothing factor, performing Kalman filtering prediction and updating iteration to obtain clock skew and drifting optimal estimation, correcting a local clock according to the clock skew and drifting optimal estimation, and performing loop iteration to realize high-precision clock synchronization. According to the method, the noise covariance can be estimated and adjusted in real time, higher synchronization precision, higher convergence speed and higher robustness can be realized in a complex environment, and the problem that high-precision and stable clock synchronization is difficult to guarantee in a non-stable and high-dynamic wireless environment by the existing method is solved.

The invention discloses an unmanned aerial vehicle multi-source fusion navigation positioning method independent of satellite signals, and belongs to the technical field of unmanned aerial vehicle docking navigation, and the method comprises the following steps: 1, building a fusion navigation positioning system; 2, performing time registration and outlier elimination; 3, solving an information distribution coefficient; 4, time-variable measurement noise estimation is carried out; 5, updating time and measurement; step 6, fusion and differential feedback of the main filter; according to the scheme, the problems of insufficient sensor redundancy, rigid multi-source fusion navigation algorithm information distribution and fault isolation mechanism and the like of the existing docking navigation positioning system are solved, so that the navigation positioning precision and the anti-interference capability of the unmanned aerial vehicle are improved.

A multiple interference cancellation circuit includes a first kernel generation circuit that receives transmission signals and, based on the transmission signals, generates a first interference model signal, a second kernel generation circuit that receives the transmission signals and, based on the plurality of transmission signals, generates a second interference model signal different from the first interference model signal, a first adaptive filter that receives the first interference model signal and, based on the first interference model signal and a first filter coefficient, generates a first interference estimation signal, a second adaptive filter that receives the second interference model signal and, based on the second interference model signal and a second filter coefficient, generates a second interference estimation signal, and a first summer that outputs a filtering signal obtained by subtracting the first interference estimation signal and the second interference estimation signal from a reception signal.

The invention discloses a grinding wheel three-dimensional landform measuring method and system based on silica gel copying and density clustering, and belongs to the technical field of precision machining.The measuring system comprises a grinding wheel landform copying device used for copying the landform of a to-be-detected area of a grinding wheel to obtain a grinding wheel landform copy; the three-dimensional measuring device is used for measuring the three-dimensional shape of the surface of the grinding wheel landform copy; the computer processing device is used for operating a three-dimensional data processing algorithm; and the three-dimensional data processing system is used for carrying out denoising and feature extraction on the three-dimensional shape of the grinding wheel based on a three-dimensional data processing algorithm. The problems that a large-size grinding wheel is difficult to directly detect, the geomorphic measurement noise of a fine-grained grinding wheel is large, and the abrasive particle recognition precision is poor are solved.

The invention provides a self-adaptive particle filtering method for a maritime maneuvering target, and solves the technical problems of mismatching of motion models and untimely response caused by frequent switching of motion states of an existing maritime maneuvering target. The method comprises the following steps: performing prediction updating on an IMM-PF filter and an MHM-PF filter introducing a multi-hypothesis idea according to target measurement data, and respectively obtaining target state estimation of the IMM-PF filter and the MHM-PF filter; calculating scores of three indexes of a target according to target state estimation of an IMM-PF filter or an MHM-PF filter, performing weighting to obtain a fused original target maneuvering scoring function, and performing smoothing to obtain a target maneuvering probability; and setting a maneuverability discrimination threshold, comparing the maneuverability probability of the target with the maneuverability discrimination threshold, and adaptively fusing the target state estimation of the IMM-PF filter and the MHM-PF filter to obtain the final state estimation of the target. The method can be widely applied to the technical field of target tracking.

A low drop-out (LDO) regulator includes an NMOS transistor having one of a source and drain terminal configured to be coupled to a voltage supply and the other of the source and drain terminal coupled to the low drop out voltage regulator output. A gate driver for the LDO regulator includes a boost converter having a boost converter input configured to be coupled to a boost converter reference voltage, a boost converter output configured to output a boosted voltage greater than the boost converter reference voltage and the voltage supply; and a boost converter clock input configured to receive a clock signal. The gate driver further includes a notch filter having a notch filter input coupled to the boost converter output and a notch filter output coupled to the gate driver output.

The application relates to the technical field of integrated circuits, and provides a method, a device, equipment and a storage medium for reducing a glitch signal in a chip circuit, the method comprising the following steps: analyzing the chip circuit to obtain circuit signals; wherein the circuit signals comprise a flip rate and a fan-out coefficient; based on the flip rate and the fan-out coefficient, identifying and screening a target glitch signal to be optimized from the circuit signals; according to the arrival time of the target glitch signal, selecting a matching target clock signal from at least two kinds of preset control clock signals with different duty cycles; and implanting a clock operation unit controlled by the target clock signal on the path of the target glitch signal to reduce the target glitch signal. The application introduces control clock signals with different duty cycles, and intelligently selects according to the arrival time of the target glitch signal, thereby enhancing the flexibility of the implantation position, and expanding the optimization space and application range of the glitch signal reduction.